WO2016201671A1 - Procédé et dispositif permettant d'extraire des caractéristiques locales d'un nuage de points en trois dimensions - Google Patents

Procédé et dispositif permettant d'extraire des caractéristiques locales d'un nuage de points en trois dimensions Download PDF

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Publication number
WO2016201671A1
WO2016201671A1 PCT/CN2015/081790 CN2015081790W WO2016201671A1 WO 2016201671 A1 WO2016201671 A1 WO 2016201671A1 CN 2015081790 W CN2015081790 W CN 2015081790W WO 2016201671 A1 WO2016201671 A1 WO 2016201671A1
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Prior art keywords
point
local feature
local
point cloud
extracted
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PCT/CN2015/081790
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English (en)
Chinese (zh)
Inventor
王文敏
镇明敏
王荣刚
李革
董胜富
王振宁
李英
高文
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北京大学深圳研究生院
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Priority to US15/575,897 priority Critical patent/US10339409B2/en
Priority to PCT/CN2015/081790 priority patent/WO2016201671A1/fr
Publication of WO2016201671A1 publication Critical patent/WO2016201671A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/64—Three-dimensional objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/40—Image enhancement or restoration using histogram techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/64—Analysis of geometric attributes of convexity or concavity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds

Definitions

  • the present application relates to a local feature extraction method and apparatus for a three-dimensional point cloud.
  • 3D digital geometric model has become the fourth digital media form after digital audio, digital image and digital video.
  • Its related basic theory and key technology research has developed into a new discipline - Digital geometry processing, and gradually in the field of computer-aided design, dynamic roaming industry, biomedicine, digital cultural heritage protection and other fields have been widely used.
  • hardware devices such as Microsoft Kinect and Primesense (a somatosensory technology device)
  • 3D (3 Dimensions, 3D) point cloud information acquisition becomes more convenient and faster.
  • 3D vision local feature extraction has always been the most critical part of point cloud processing, and local feature descriptors are used to describe the local features of the extracted point cloud. Therefore, whether it is 3D object recognition or 3D reconstruction, local feature descriptors play a very important role.
  • 3D local feature descriptors 3D local feature extraction
  • signature which calculates a signature for the local point cloud as its feature description, mainly including points.
  • Signature (3D Point Fingerprint), 3D-SURF, etc.
  • histogram which is a local feature description of the local point cloud computing histogram, mainly including rotating images ( Spin Image), 3D Shape Contexts
  • SHOT 3D local feature descriptor
  • the SHOT descriptor has the advantages of both signature and histogram. Good to use in 3D point cloud processing.
  • the three 3D local feature descriptors ignore the concavo-convex features of the point cloud surface, so that the extracted local features are easy to produce ambiguity, and thus the estimation is often inaccurate in the processing of the three-dimensional point cloud. The situation happened.
  • the accuracy of prior art local feature extraction needs to be improved.
  • the present application provides a local feature extraction method and device for a three-dimensional point cloud, which can improve the extraction accuracy of local features of a three-dimensional point cloud.
  • a local feature extraction method for a three-dimensional point cloud includes: separately calculating a local feature point to be extracted and each voxel in a preset point cloud sphere The angle information of the point of the prime and the concave and convex information of the curved surface between the local feature point to be extracted and the point of each of the body elements, the preset point cloud sphere containing a plurality of individual elements, the body An element is adjacent to the local feature point to be extracted; performing histogram statistics according to the angle information and the concave and convex information, generating a histogram corresponding to each of the body elements; and a preset point cloud sphere Each of the histograms corresponding to each of the body elements is connected to obtain an extraction vector; the extraction vector is subjected to exponential normalization processing and second normalization normalization processing.
  • a local feature extraction device for a three-dimensional point cloud includes: a first calculation unit, configured to separately calculate a local feature point to be extracted and each body element in a preset point cloud sphere An angle calculation information of the point; and a second calculation unit, configured to calculate concave and convex information of the curved surface between the local feature point to be extracted and the point of the body element, where the preset point cloud sphere includes several individuals An element, the body element is adjacent to the local feature point to be extracted; a statistical unit, configured to perform a histogram according to the angle information calculated by the first calculating unit and the concave and convex information calculated by the second calculating unit a graph, generating a histogram corresponding to each of the body elements, and a vector extracting unit, configured to compare the statistics of the statistical unit with each of the body elements in the preset point cloud sphere Each of the histograms is connected to obtain an extraction vector; a normalization processing unit is configured to
  • the local feature extraction method and apparatus for the three-dimensional point cloud calculates the angle information and the concave and convex information of the point of the feature point to be extracted and the adjacent body element based on the local reference frame corresponding to the point of each body element, and can accurately Calculate the characteristic relationship between two points, which has the property of translation and rotation invariance, and because the extraction also includes the concave and convex information of the local point cloud, it solves the previous 3D local feature description and ignores the ambiguity of the concave and convex, which leads to the extraction inaccuracy.
  • the problem In the normalization process, the exponential normalization process and the second paradigm normalization process are used to solve the problem that the similarity calculation caused by the small or too small elements in the vector is inaccurate when the feature extraction is performed, so that Improve the accuracy of the extracted three-dimensional local features.
  • FIG. 1 is a flow chart of a method for extracting local features of a three-dimensional point cloud according to the present invention
  • FIG. 2 is another flow chart of a method for extracting local features of a three-dimensional point cloud according to the present invention
  • 3 is a flow chart of determining a local reference frame where points of each body element are located
  • Figure 4 is a calculation of the local feature points to be extracted and each body element in the preset point cloud sphere Point angle information flow chart
  • Figure 5 is a schematic diagram of angle information between local reference frames between two points
  • Figure 6a is a characterization sub-histogram obtained by normalization using the second normal form
  • Figure 6b is a characterization sub-histogram obtained by exponential normalization and second normal normalization
  • Figure 7a is a line graph of recall and accuracy for different parameters a in a data set colorless point cloud
  • Figure 7b is a line graph of recall rates and accuracy rates for different parameters a in a real point cloud scenario
  • 11 is a comparison result of a local feature descriptor obtained by using an application method in a real scene and other feature descriptors;
  • FIG. 12 is a schematic structural diagram of a device according to an embodiment of the present invention.
  • FIG. 13 is a schematic structural diagram of another device according to an embodiment of the present invention.
  • a local feature extraction method for a three-dimensional point cloud is provided, which can improve the extraction accuracy of local features of the three-dimensional point cloud.
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • FIG. 1 is a flowchart of a method for extracting local features of a three-dimensional point cloud according to an embodiment of the present invention.
  • a local feature extraction method for a three-dimensional point cloud may include the following steps:
  • the preset point cloud sphere includes a plurality of individual elements, and the body element is adjacent to the local feature points to be extracted.
  • the angle information of the point of each body element in the preset point cloud sphere and the point of the surface point between each local element point and each body element are calculated. It is not calculated based on the traditional coordinate system.
  • the embodiment of the present application designs different local reference systems for the points of each body element. Specifically, the covariance matrix is first calculated, and then the eigen decomposition is performed on the matrix to obtain the values of the three eigenvectors, and then the eigenvectors are scaled from large to large. Small order sorting, final alignment to do ambiguity calculation, get the local reference system where the points of the body element are located.
  • a histogram corresponding to each body element is generated.
  • the local feature extraction method of the three-dimensional point cloud calculates the angle information and the concave and convex information of the point of the feature point to be extracted and the adjacent body element based on the local reference system corresponding to the point of each body element, and can accurately calculate two
  • the characteristic relationship between points has the property of translation and rotation invariance, and the extraction of the concave and convex information of the local point cloud is also included in the extraction, which solves the problem of negligible ambiguity caused by the previous 3D local feature description. .
  • the exponential normalization process and the second paradigm normalization process are used to solve the problem that the similarity calculation caused by the small or too small elements in the vector is inaccurate when the feature extraction is performed. Therefore, the three-dimensional local features extracted by the method of the present application are more accurate.
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • FIG. 2 is a flowchart of a method for extracting local features of a three-dimensional point cloud according to an embodiment of the present invention. As shown in FIG. 2, the embodiment may include the following steps:
  • the point cloud sphere is segmented along the direction angle, the elevation angle, and the radius of the point cloud sphere to obtain a plurality of body elements adjacent to the local feature points to be extracted.
  • R is the radius of the point cloud sphere
  • p' is the point of the body element
  • p is the local feature point
  • di
  • the specific process is as follows: 204A, determining an angle ⁇ between a roll axis of a local reference frame where a point of a body element is located and a roll axis of a coordinate system in which the local feature point is located, and a local reference frame The angle ⁇ between the heading axis and the heading axis of the coordinate system in which the local feature point is located, and the angle ⁇ between the pitch axis of the local reference system and the pitch axis of the coordinate system in which the local feature point is located.
  • a KD tree (a k-dimensional tree, a data structure that splits the k-dimensional data space) is used to search for the domain points of the feature points. What is determined in this way is a point cloud sphere with a feature point as the center of the sphere.
  • the bump information and angle information between each point and the feature point are calculated, and then a histogram of the body element is obtained.
  • a local reference system for points of each body element is used in the calculation process.
  • the estimation of the local reference system mainly includes the following steps:
  • R is the radius of the point cloud sphere
  • p' is the point of the body element
  • p is the local feature point
  • di
  • the feature values corresponding to the feature vector are sorted in descending order, and the corresponding three feature vectors are the roll axis x of the local reference system, the heading axis y, and the pitch axis z.
  • N(x), N(y) represent the normals of points x and y, respectively.
  • D the concavity and convexity of the surface between the two points, where the judgment of the symbol D is as follows:
  • p denotes the local feature point to be extracted and p' denotes the point of the body element.
  • the histogram ⁇ corresponding to the body element is calculated by combining the two pieces of information:
  • ⁇ is the last used to describe the angle information and the bump information between the neighborhood point and the feature point. Based on the obtained ⁇ , the histogram position at which the neighborhood point falls can be determined.
  • the final operation for the descriptor is normalization, where the exponential normalization and the second normalization are used.
  • the index normalization is actually the index calculation of each component of the feature, which is represented by the function f as follows:
  • the function f is used for calculation, and the obtained descriptor is normalized by the second normal form to obtain the final 3D local feature descriptor based on the unique angle histogram signature.
  • the histogram is normalized using only the second normal form, as shown in Fig. 6b, the histogram is normalized by exponential normalization and second normal. It can be seen that the histogram normalized by the index (Fig. 6b) appears smoother, which is more accurate for the characterization, and does not make the descriptor affect the last because some descriptor components are too high or too low. Match results.
  • 3D local feature descriptors based on unique angle histogram signatures it can be used not only for local feature description of point clouds without RGB (a color standard of industry), but also for point clouds with RGB information. Describe.
  • the 3D local feature descriptor (SUAH) obtained by the method of the present application has better results than other feature descriptors (SHOT, ISI) under different noises;
  • the 3D local feature descriptors (SUAH and CSUAH) obtained by the method of the present application have better effects than other feature descriptors (SHOT, CSHOT, ISI).
  • Embodiment 3 is a diagrammatic representation of Embodiment 3
  • FIG. 12 is a schematic structural diagram of a device according to an embodiment of the present invention.
  • a local feature extraction device for a three-dimensional point cloud may include:
  • a first calculating unit 60A configured to separately calculate angle information of a point of the local feature point to be extracted and a point of each body element in the preset point cloud sphere
  • a second calculating unit 60B configured to calculate the part to be extracted
  • the concave and convex information of the curved surface between the feature point and the point of the body element, the preset point cloud sphere includes a plurality of individual elements adjacent to the local feature point to be extracted.
  • the statistic unit 61 is configured to perform histogram statistics according to the angle information calculated by the first calculating unit 60A and the concave and convex information calculated by the second calculating unit 60B, and generate a histogram corresponding to each of the body elements.
  • the vector extraction unit 62 is configured to connect the respective histograms corresponding to each of the body elements in the preset point cloud sphere, which are counted by the statistics unit 61, to obtain an extraction vector;
  • the normalization processing unit 63 is configured to perform an exponential normalization process and a second normalization normalization process on the extracted vector extracted by the vector extracting unit 62.
  • the apparatus of the embodiment of the present invention may further include: a constructing unit 64, configured to construct a point cloud sphere with the local feature point to be extracted as a center of the sphere and a preset length as a radius.
  • a constructing unit 64 configured to construct a point cloud sphere with the local feature point to be extracted as a center of the sphere and a preset length as a radius.
  • the dividing unit 65 is configured to divide the point cloud sphere along a direction angle, an elevation angle, and a radius of the point cloud sphere to obtain a plurality of body elements adjacent to the local feature point to be extracted.
  • the apparatus of the embodiment of the present invention further includes: a determining unit 66, configured to determine a local reference system where a point of each body element is located, and the determining unit 66 specifically includes:
  • the calculating module 660 is configured to calculate the covariance matrix M according to the formula (1):
  • R is the radius of the point cloud sphere
  • p' is the point of the body element
  • p is the local feature point
  • di
  • the decomposition module 661 is configured to perform feature decomposition on the matrix M to obtain values of three feature vectors.
  • the sorting module 662 is configured to sort the feature vectors in descending order, as the roll axis x, the heading axis y, and the pitch axis z of the local reference system, respectively.
  • the first calculating unit 60A is specifically configured to:
  • the second calculating unit 60B is specifically configured to:
  • the statistic unit 61 is specifically configured to calculate each of the bodies according to the angle information ⁇ calculated by the second calculating unit 60B and the angle information ⁇ calculated by the first calculating unit 60A, in combination with the formula (3).
  • the histogram ⁇ corresponding to the elements one by one.

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Abstract

La présente invention concerne un procédé et un dispositif permettant d'extraire des caractéristiques locales d'un nuage de points en trois dimensions. Des informations d'angle et des informations concavo-convexes concernant un point de caractéristique qui doit être extrait, et un point d'un élément de corps adjacent sont calculées sur la base d'un système de référence local correspondant à des points de chaque élément de corps et une relation de caractéristiques entre les deux points peut être calculée avec précision. La propriété d'invariance par translation et rotation est détenue. De plus, puisque des informations concavo-convexes concernant un nuage de points locaux sont contenues pendant l'extraction, le problème d'une extraction imprécise provoquée en ignorant l'ambiguïté concavo-convexe dans une précédente description de caractéristiques locales en 3D est résolu. Pendant un traitement de normalisation, un traitement de normalisation exponentiel et un traitement de normalisation en seconde forme normale sont adoptés et le problème d'un calcul de similarité imprécis provoqué par une circonstance dans laquelle quelques éléments d'un vecteur sont trop importants ou trop petits pendant l'extraction de caractéristiques, est résolu de telle sorte que la précision des caractéristiques locales en trois dimensions extraites puisse être améliorée.
PCT/CN2015/081790 2015-06-18 2015-06-18 Procédé et dispositif permettant d'extraire des caractéristiques locales d'un nuage de points en trois dimensions WO2016201671A1 (fr)

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CN109215129B (zh) * 2017-07-05 2022-10-04 中国科学院沈阳自动化研究所 一种基于三维点云的局部特征描述方法
CN107424166A (zh) * 2017-07-18 2017-12-01 深圳市速腾聚创科技有限公司 点云分割方法及装置
CN108564096A (zh) * 2018-04-26 2018-09-21 电子科技大学 一种邻域拟合rcs序列特征提取方法
CN113837952A (zh) * 2020-06-24 2021-12-24 影石创新科技股份有限公司 基于法向量的三维点云降噪方法、装置、计算机可读存储介质及电子设备
CN112750144A (zh) * 2020-12-28 2021-05-04 西安理工大学 点云匹配中一种基于特征直方图的点云特征提取方法
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CN113177477A (zh) * 2021-04-29 2021-07-27 湖南大学 一种基于三维点云分析的目标检测识别方法
CN113177555A (zh) * 2021-05-21 2021-07-27 西南大学 基于跨层级跨尺度跨注意力机制的目标处理方法及装置
CN113177555B (zh) * 2021-05-21 2022-11-04 西南大学 基于跨层级跨尺度跨注意力机制的目标处理方法及装置

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